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      Inkjet-printed unclonable quantum dot fluorescent anti-counterfeiting labels with artificial intelligence authentication

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          Abstract

          An ideal anti-counterfeiting technique has to be inexpensive, mass-producible, nondestructive, unclonable and convenient for authentication. Although many anti-counterfeiting technologies have been developed, very few of them fulfill all the above requirements. Here we report a non-destructive, inkjet-printable, artificial intelligence (AI)-decodable and unclonable security label. The stochastic pinning points at the three-phase contact line of the ink droplets is crucial for the successful inkjet printing of the unclonable security labels. Upon the solvent evaporation, the three-phase contact lines are pinned around the pinning points, where the quantum dots in the ink droplets deposited on, forming physically unclonable flower-like patterns. By utilizing the RGB emission quantum dots, full-color fluorescence security labels can be produced. A convenient and reliable AI-based authentication strategy is developed, allowing for the fast authentication of the covert, unclonable flower-like dot patterns with different sharpness, brightness, rotations, amplifications and the mixture of these parameters.

          Abstract

          Anti-counterfeiting technologies should ideally be unclonable, yet simple to fabricate and decode. Here, the authors develop an inkjet-printable and unclonable security label based on random patterning of quantum dot inks, and accompany it with an artificial intelligence decoding mechanism capable of authenticating the patterns.

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          Most cited references43

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          Planning chemical syntheses with deep neural networks and symbolic AI

          To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes.
            • Record: found
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            • Article: not found

            50-Fold EQE Improvement up to 6.27% of Solution-Processed All-Inorganic Perovskite CsPbBr3QLEDs via Surface Ligand Density Control

              • Record: found
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              • Article: not found

              Printing colour at the optical diffraction limit.

              The highest possible resolution for printed colour images is determined by the diffraction limit of visible light. To achieve this limit, individual colour elements (or pixels) with a pitch of 250 nm are required, translating into printed images at a resolution of ∼100,000 dots per inch (d.p.i.). However, methods for dispensing multiple colourants or fabricating structural colour through plasmonic structures have insufficient resolution and limited scalability. Here, we present a non-colourant method that achieves bright-field colour prints with resolutions up to the optical diffraction limit. Colour information is encoded in the dimensional parameters of metal nanostructures, so that tuning their plasmon resonance determines the colours of the individual pixels. Our colour-mapping strategy produces images with both sharp colour changes and fine tonal variations, is amenable to large-volume colour printing via nanoimprint lithography, and could be useful in making microimages for security, steganography, nanoscale optical filters and high-density spectrally encoded optical data storage.

                Author and article information

                Contributors
                fsli@fzu.edu.cn
                yuanhui.zheng@fzu.edu.cn
                qianlei@tcl.com
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 June 2019
                3 June 2019
                2019
                : 10
                : 2409
                Affiliations
                [1 ]ISNI 0000 0001 0130 6528, GRID grid.411604.6, Institute of Optoelectronic Technology, , Fuzhou University, ; Fuzhou, 350116 China
                [2 ]ISNI 0000 0001 0130 6528, GRID grid.411604.6, College of Chemistry, , Fuzhou University, ; Fuzhou, 350116 China
                [3 ]Guangdong Poly Optoelectronics Co., Ltd, Jiangmen, 529020 China
                [4 ]TCL Corporate Research, No. 1001 Zhongshan Park Road, Nanshan District, Shenzhen, 518067 China
                Author information
                http://orcid.org/0000-0001-5299-2812
                http://orcid.org/0000-0001-6326-727X
                Article
                10406
                10.1038/s41467-019-10406-7
                6547729
                31160579
                733305dd-2562-4ccb-bd4d-56dc3cbf2036
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 October 2018
                : 8 May 2019
                Categories
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                Custom metadata
                © The Author(s) 2019

                Uncategorized
                nanoscale materials,quantum dots
                Uncategorized
                nanoscale materials, quantum dots

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